In this research the development of a signal timing optimization model for oversaturated urban traffic networks with stochastic driver behavior and vehicle arrival headway is presented. The model is called Intelligent Dynamic Signal Timing Optimization Program or IDSTOP. IDSTOP is formulated as a dynamic optimization problem whose objective is to maximize the number of weighted completed trips in the network (weighted by the length of the shortest route available for that trip). The model aims at managing transportation supply by optimizing signal timing parameters and simultaneously managing transportation demand by redirecting vehicles to less congested routes.
Solving IDSTOP is a very complicated task since it is a nonlinear optimization program with no closed form formulation for the objective function in terms of the decision variables; and has an extremely large decision space. Therefore, a meta-heuristic algorithm is developed. It creates a population of candidate solutions and improves their quality over different generations. To reduce the runtime, a heuristic method was developed to create feasible solutions for the first population. The feasibility of candidate solutions was first checked using a macroscopic approach. A microscopic approach was also used to check all the solutions that were marked feasible by the macroscopic approach. To account for stochastic driver behavior and vehicle arrival headway, several microscopic simulation replications were made. The fittest individual of each population was chosen for traffic assignment. Assigning traffic for the fittest individual not only significantly reduced the runtime, but also insured not using inefficient signal timing parameters.
IDSTOP solutions were compared to Direct-CORSIM solution using a realistic case study network and four demand patterns covering both undersaturated and oversaturated conditions for symmetric and asymmetric traffic demands. Findings indicated that IDSTOP solutions resulted in significantly more efficient network performance than Direct-CORSIM solutions. IDSTOP solutions increased the number of completed trips by 2.0% to 19.6% and at the same time reduced average delay by 8.9% to 30.8% for different demand patterns in the case study network. These figures indicated significant improvement in the network performance.
Simple GA, Elitist simple GA, Micro-Elitist GA, self-adaptive ES, and Elitist self-adaptive ES (ES+) were used to solve IDSTOP. In general, ES+ outperformed the rest of algorithms in reaching most different levels of the upper-bounds. In addition, ES+ was very efficient in oversaturated conditions especially when demand was symmetric. Micro-Elitist GA was very quick in early improvements in the fitness value. However, in most of the cases it was outperformed by ES+ in reaching higher levels of fitness value except for asymmetric undersaturated conditions.
Using IDSTOP, Optimal Left Turn Management Program (OLTMP) was developed. OLTMP improves network performance by prohibiting the left turns at certain intersections of the network. Numerical findings indicated that OLTMP had great potential to improve network performance efficiency by optimizing the policies on the left turns. When left turn volume was low (up to 7.5% of the capacity of a lane), none of the left turns were prohibited since left-turners had enough opportunity to make their turning maneuver in permitted phases. When left turn volume was very high (20% of the capacity of a lane), none of the left turns were prohibited as well because doing so resulted in rerouting too many vehicles and overcrowding other intersections. However, for moderate left turn volumes (10% to 17.5% of the capacity of a lane) left turns were prohibited in one or two intersections of the network.
A method was proposed to determine the policy that resulted in a more efficient network performance among variable cycles and common cycle policies. Our findings in a case study network (symmetric oversaturated demand pattern) that was suitable for signal coordination indicated the variable cycle length strategy has great potential to improve network performance compared to common cycle strategy. The improvement is achieved by using more suitable signal timing parameters for each intersection and only coordinating them when needed. In the case study, variable cycle lengths strategy reduced total delay by 7.5%, and improved the number of completed trips by 1.0% compared to common cycle length strategy. Therefore, using variable cycle lengths significantly improved network performance efficiency in symmetric oversaturated conditions.
IDSTOP was used to develop Optimal Network Metering Program (ONMP). ONMP improved network performance by metering traffic at entry points of the network. ONMP was formulated and a meta-heuristic algorithm was developed to solve it. The numerical findings showed that optimized metering strategy reduced total delay by 10.6% and total travel time by 6.7% compared to no metering strategy. Therefore, optimal metering has significantly improved network performance in the case study. In addition, optimized metering strategy reduced total delay by 4.5% and total travel time by 2.7% compared to the best uniform metering strategy. This indicated that ONMP solution significantly improved network performance compared to the best uniform metering strategy.